RAN energy consumption is a major OPEX source for mobile telecom operators, and 5G is expected to increase these costs by several folds. Moreover, paradigm-shifting aspects of the 5G RAN architecture like RAN disaggregation, virtualization and cloudification introduce new traffic-dependent resource management decisions that make the problem of energy-efficient 5G RAN orchestration harder. To address such a challenge, we present a first comprehensive virtualized RAN (vRAN) system model aligned with 5G RAN specifications, which embeds realistic and dynamic models for computational load and energy consumption costs. We then formulate the vRAN energy consumption optimization as an integer quadratic programming problem, whose NP-hard nature leads us to develop GreenRAN, a novel, computationally efficient and distributed solution that leverages Lagrangian decomposition and simulated annealing. Evaluations with real-world mobile traffic data for a large metropolitan area are another novel aspect of this work, and show that our approach yields energy efficiency gains up to 25% and 42%, over state-of-the-art and baseline traditional RAN approaches, respectively.

We study the dynamic energy optimization problem in data centers. We formulate and solve the following offline problem: given a set of jobs to process, where the jobs are characterized by arrival instances, required processing time, and completion deadlines, and given the energy requirements of switching servers ON or OFF, in which time-slot which server has to be assigned to which job, and in which time-slot which server has to be switched ON or OFF, so that the total energy is optimal for some time horizon. We formulate the offline problem as a binary integer program that can be considered as a new version of generalized assignment problem which includes new constraints stemming from deadline characteristics of jobs and the activation energy of servers. We propose an online algorithm that solves the problem heuristically, and we compare it to random assignment solution.

The coexistence of LTE-Unlicensed (LTE-U) and WiFi in unlicensed spectrum is studied in the context of airtime sharing. We consider core problem where a set of LTE-U cells from different operators share the same channel as a co-located WiFi access point (AP). We assume that LTE-U cells utilize Listen-Before-Talk (LBT) as the default channel access mechanism. Principally, we deal with the following question: how should an operator's LTE-U cell adjust its contention window in order to provide a fair coexistence both with WiFi and co-located LTE-U cells of other operators? We consider that LTE-U cells behave altruistically both among themselves and to WiFi. Cooperation of LTE-U cells is studied using a coalition formation game framework which is based on the well-known Shapley value. We define a payoff configuration scheme in the coalition game which involves altruism. We prove that the coalitional game is always zero-monotonic, and Shapley value is also max-min fair. We compare airtime sharing performance of Shapley value with weighted proportional fairness via numerical results and show that Shapley value provides much better fairness than proportional fairness as determined by entropy and Jain's index metrics while having roughly equal average airtime.

A matching theoretic approach to study the partner selection in cooperative relaying is followed. Partner selection is considered as a special stable roommate problem where each player ranks its partners by some criterion. Each agent aims here at finding a “good” partner in order to exploit efficiently the spatial diversity achieved with cooperation. We adapt Irving's algorithm [3] for determining the partners of each player. The ranking criterion here is chosen to be outage probability such that each player comprises its own preference list according to outage probability from the lowest to the highest. The first player in the preference list provides the lowest outage probability. We introduce a decentralized version of Irving's algorithm. Then, we compare the results obtained by stable-matching with the global optimum and random selection results. From the computational results, we observe that stable-matching results are near to global optimum as well as superior than random selection in terms of average outage probability.

This paper studies a significant problem in green networking called switching off base stations in case of cooperating service providers by means of stochastic geometric and coalitional game tools. The coalitional game herein considered is played by service providers who cooperate in switching off base stations. When they cooperate, any mobile is associated to the nearest BS of any service provider. Given a Poisson point process deployment model of nodes over an area and switching off base stations with some probability, it is proved that the distribution of signal to interference plus noise ratio remains unchanged while the transmission power is increased up to preserving the quality of service. The coalitional game behavior of a typical player is called to be hedonic if the gain of any player depends solely on the members of the coalition to which the player belongs, thus, the coalitions form as a result of the preferences of the players over their possible coalitions' set. We utilize the Nash-stable core for determining the coalitions of service providers.

We consider a common object (data) that each mobile in the medium is interested to receive, and which can be obtained from any base station transmitting the data. For example, the broadcast object could be streaming transmission of a sport or cultural event, or it could be some signaling such as a beacon for time synchronization or for power control purposes. This problem can be conceived as a coalition game played by mobiles which we call as association game of mobiles. This game has an incentive to form grand coalition where all players join to the game. We prove that using Bondareva-Shapley theorem, this coalition game has a non-empty core which means that grand coalition is stable. Then, we examine the cost allocation policy for different methods such as egalitarian allocation, proportional repartition of total cost, the Shapley value and the nucleolus. We also conclude that if the nucleolus is used as the cost allocation algorithm, the players maintain the grand coalition satisfying the minimization of total cost for broadcast transmission.

This paper presents a novel cooperative transmission scheme based on superposition modulation and incremental relaying. Incremental relaying is provided by a hybrid automatic-repeat-request (ARQ) type feedback channel. In the proposed scheme, users utilize decode-and-forward method with superposition modulation in which each user superimposes partner's signal if the destination has sent a negative acknowledgement (NACK) in the previous slot. In other words, relaying is performed under the control of feedback data. In case of an acknowledgment (ACK) from the destination, the users transmit merely their own signal. It is shown that at low signal-to-noise ratio (SNR) values the proposed scheme has a better diversity gain than the classical superposition modulated cooperative transmission and is about 1.5-2 dB better in error performance than the existing superposition based decode-and-forward cooperative scheme which means that incremental relaying brings significant gain in SNR.